(Not modified) Liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS) is a widely used platform for high-throughput identification and quantification of proteins in biological samples. In addition to experimental steps in the pipeline, computational and statistical procedures play important roles in determining the content of the mixture. However, even with the best analytical platforms and modern software, only a small fraction of spectra are typically identified, thus directly impacting the quality of the biological sample analysis. If high- throughput proteomics techniques are to become routinely used in biomedical applications on the population scale, it is critical to address analytical and computational factors that contribute to the inadequate identification coverage and sensitivity. Over the past several years, we and others have spent a significant amount of research activity to understand and model analytical platforms and subsequently improve computational methods for the analyses of complex biological mixtures. While our original grant application has resulted in methods and programs already accepted by the community, there is a need and significant room for further key contributions. We see many of these contributions being related to the analyses of dynamic changes in cells and tissues, and involving changes in protein quantities, protein post-translational modifications (PTMs) and transient protein-protein interactions. Mass spectrometry-based proteomics provides an excellent platform to address each of these challenges. Thus, we plan to continue to develop novel methods for label-free quantification and remain close to our core strengths, but also strongly focus on PTMs and protein-protein interactions as new directions of this renewal application. This application includes a considerably closer collaboration between computational (Dr. Radivojac, Dr. Tang) and experimental (Dr. Arnold, Dr. Clemmer, Dr. Reilly) scientists than did our original application. The investigators bring complementary expertise and experience in a range of disciplines involving protein bioinformatics, algorithms, machine learning, as well as analytical chemistry and instrumentation. Overall, we believe that this proposal will result in significant advances for mass spectrometry-based proteomics.